CTA: How could the findings of this analysis link to clinical outcomes for patients with breast cancer? Can you provide an example?

Dr Bakal: There is an explosion of research, especially in the clinic, of using sequencing as a diagnostic and research tool. We are looking for mutations that are going to be driving cancer in the tumor cell. The idea being that if we identify mutations in a patient’s cancer cells, we hope we will be able to treat the patients based on these specific mutational profiles. But I think our study emphasizes the idea that cancer is not driven solely by mutational events you can detect by sequencing. So in order to treat a cancer effectively, the diagnosis must involve different types of technologies that analyze the cancer in different ways.

For example, changes in cell shape could contribute to cancer in a way that has nothing to do with the mutation. These shape changes could be caused by, for example, mechanical forces that tumors cells are exposed to, caused by stromal components of tumors. These are things that don’t necessarily have to with the mutation status of a tumor cell. When we are doing a diagnosis, we have to appreciate that the cancer can be driven both by sequence events but also by “non-genetic” events. That affects our treatment. We might want to treat, not just based on a patient’s mutational profile, but also cell shape, pathology, tissue architecture, etc.

CTA: How might your findings and this shape-gene network help to make existing therapies more effective for patients?

Dr Bakal: What our network is useful for is explaining this relationship between cancer cell shape and going back to find the players linking transcription and cell shape.

Let’s imagine that cells are being forced into a certain shape that is driving cancer. We now have targets in our network that we can use to disrupt this connection or manipulate the connection. We did not have that before. We think this link between shape and cancer is important, but really what this gives us is players in the network that can be targeted therapeutically. These targets might not be mutated. They might be targets that would not have come out in a sequence analysis but could be important to affect cancer behavior. If we were going to try to manipulate the shape of a cell in a way to give a better therapy, these are providing us the targets with which to do that.

CTA: Is this type of research something that could be applied to other solid tumor types? How so?

Dr Bakal: We are trying to do that now. This worked very well in breast cancer, and one of the reasons it worked well is because of the wealth of data we have available for that particular disease. We could repeat this analysis in other solid tumor types, but we are probably missing data that we would desperately need to do it.